Inferring time-varying selection for HIV escape from T cell responses

Evolution in natural populations is complicated, as natural selection can act on multiple traits with effects that vary along with the environment. One example is provided by HIV-1 infection, where the virus faces pressure to evade host immunity while also preserving replicative fitness. In this case, a single mutation may affect both replication and immune escape. Furthermore, the strength of selection for immune escape may vary over time along with the concentration of immune cells that target the virus. Temporal genetic data can help us to understand evolution quantitatively, but new methods are needed to extract such complex and time-varying features from data. Here we extend the marginal path likelihood [1] method to disentangle the time-varying effects of escaping CD8⁺ T cell-mediated immunity, which we model as a binary trait, from other contributions to fitness. After validation in simulations, we applied this model to study within-host HIV-1 evolution in a clinical data set. We find selection for immune escape in T cell epitopes that drops sharply along with falling populations of immune cells. We also see a general trend toward weaker selection late in infection. Our approach is not limited to HIV-1 evolution and could also be adapted to study the evolution of quantitative traits in other contexts.

[1] Sohail, Muhammad Saqib, et al. "MPL resolves genetic linkage in fitness inference from complex evolutionary histories." Nature Biotechnology 39.4 (2021): 472-479.